_base_ = "fcos_r50_caffe_fpn_gn-head_1x_coco.py"

model = dict(
    # pretrained='open-mmlab://detectron2/resnet50_caffe',
    backbone=dict(
        dcn=dict(type="DCNv2", deform_groups=1, fallback_on_stride=False),
        stage_with_dcn=(False, True, True, True),
    ),
    bbox_head=dict(
        norm_on_bbox=True,
        centerness_on_reg=True,
        dcn_on_last_conv=True,
        center_sampling=True,
        conv_bias=True,
        loss_bbox=dict(type="GIoULoss", loss_weight=1.0),
    ),
    # training and testing settings
    test_cfg=dict(nms=dict(type="nms", iou_threshold=0.6)),
)

# dataset settings
img_norm_cfg = dict(mean=[103.530, 116.280, 123.675], std=[1.0, 1.0, 1.0], to_rgb=False)
train_pipeline = [
    dict(type="LoadImageFromFile"),
    dict(type="LoadAnnotations", with_bbox=True),
    dict(type="Resize", img_scale=(1333, 800), keep_ratio=True),
    dict(type="RandomFlip", flip_ratio=0.5),
    dict(type="Normalize", **img_norm_cfg),
    dict(type="Pad", size_divisor=32),
    dict(type="DefaultFormatBundle"),
    dict(type="Collect", keys=["img", "gt_bboxes", "gt_labels"]),
]
test_pipeline = [
    dict(type="LoadImageFromFile"),
    dict(
        type="MultiScaleFlipAug",
        img_scale=(1333, 800),
        flip=False,
        transforms=[
            dict(type="Resize", keep_ratio=True),
            dict(type="RandomFlip"),
            dict(type="Normalize", **img_norm_cfg),
            dict(type="Pad", size_divisor=32),
            dict(type="ImageToTensor", keys=["img"]),
            dict(type="Collect", keys=["img"]),
        ],
    ),
]
data = dict(
    samples_per_gpu=2,
    workers_per_gpu=2,
    train=dict(pipeline=train_pipeline),
    val=dict(pipeline=test_pipeline),
    test=dict(pipeline=test_pipeline),
)
optimizer_config = dict(_delete_=True, grad_clip=None)

lr_config = dict(warmup="linear")
